A Multifunctional Polymeric Nanotheranostic System Delivers Doxorubicin and Imaging Agents across the Blood–Brain Barrier Targeting Brain Metastases of Breast Cancer
Bibliographic record
Abstract
Metastatic brain cancers, in particular cancers with multiple lesions, are one of the most difficult malignancies to treat owing to their location and aggressiveness. Chemotherapy for brain metastases offers some hope. However, its efficacy is severely limited as most chemotherapeutic agents are incapable of crossing the blood-brain barrier (BBB) efficiently. Thus, a multifunctional nanotheranostic system based on poly(methacrylic acid)-polysorbate 80-grafted-starch was designed herein for the delivery of BBB-impermeable imaging and therapeutic agents to brain metastases of breast cancer. In vivo magnetic resonance imaging and confocal fluorescence microscopy were used to confirm extravasation of gadolinium and dye-loaded nanoparticles from intact brain microvessels in healthy mice. The targetability of doxorubicin (Dox)-loaded nanoparticles to intracranially established brain metastases of breast cancer was evaluated using whole body and ex vivo fluorescence imaging of the brain. Coexistence of nanoparticles and Dox in brain metastatic lesions was further confirmed by histological and microscopic examination of dissected brain tissue. Immuno-histochemical staining for caspase-3 and terminal-deoxynucleotidyl transferase dUTP nick end labeling for DNA fragmentation in tumor-bearing brain sections revealed that Dox-loaded nanoparticles selectively induced cancer cell apoptosis 24 h post-injection, while sparing normal brain cells from harm. Such effects were not observed in the mice treated with free Dox. Treatment with Dox-loaded nanoparticles significantly inhibited brain tumor growth compared to free Dox at the same dose as assessed by in vivo bioluminescence imaging of the brain metastases. These findings suggest that the multifunctional nanoparticles are promising for the treatment of brain metastases.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".